Zilber Daniel, Messier Kyle P, House John, Parham Fred, Auerbach Scott S, Wheeler Matthew W
Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States.
Division of Translational Toxicology, Predictive Toxicology Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, United States.
Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btaf008.
Estimating a toxic reference point using tools like the benchmark dose (BMD) is a critical step in setting policy to regulate pollution and ensure safe environments. Toxicity can be measured for different endpoints, including changes in gene expression and histopathology for various tissues, and is typically explored one gene or tissue at a time in a univariate setting that ignores correlation. In this work, we develop a multivariate estimation procedure to estimate the BMD for specified gene sets. Our approach extends the foundational univariate approach by accounting for correlation in a statistically principled way.
We illustrate the method using data from a 5-day rat study and Hallmark gene sets and compare to existing BMD results computed by the EPA for both gene sets and apical histopathology endpoints. In contrast to previous ad-hoc methods, our principled approach provides the needed extension to bring the foundational univariate method into the multivariate world of transcriptomics. In addition to use in a regulatory setting, our method can provide hypothesis generation when gene sets correspond to mechanistic pathways.
BS-BMD is implemented in R and C++ and available at https://github.com/NIEHS/BS-BMD.
使用基准剂量（BMD）等工具来估计毒性参考点，是制定污染监管政策和确保环境安全的关键步骤。毒性可以针对不同的终点进行测量，包括各种组织的基因表达变化和组织病理学变化，并且通常在忽略相关性的单变量设置中一次研究一个基因或组织。在这项工作中，我们开发了一种多变量估计程序，用于估计特定基因集的BMD。我们的方法通过以统计上合理的方式考虑相关性，扩展了基础单变量方法。
我们使用来自一项为期5天的大鼠研究的数据和标志性基因集来说明该方法，并与美国环境保护局（EPA）针对基因集和顶端组织病理学终点计算的现有BMD结果进行比较。与以前的临时方法不同，我们的合理方法提供了必要的扩展，将基础单变量方法带入转录组学的多变量领域。除了用于监管环境外，当基因集对应于机制途径时，我们的方法还可以提供假设生成。
BS - BMD用R和C++实现，可在https://github.com/NIEHS/BS-BMD获取。
